
To evaluate the performance of the synthesized images as shown in Figure 3,
two key evaluation criteria were adopted in our paper; the Spearman’s Rank Order Correlation Coefficient (SROCC) and the Linear Correlation Coefficient (LCC) [1]}.
SROCC is a measure of the monotonic relationship between the ground-truth and model prediction, while the LCC is a measure of the linear correlation between the ground-truth and model prediction. Table 1 and 2 shows the SROCC and LCC performance of the competing IQA methods for different distortion types, respectively.
In general, our model performs competitively among most distortion types. Compared with BPSQM, our model shows more performance of about 4.5% overall in dealing with the distortion of AGN, SCN, HFN, JPEG and MN, respectively as indicated in Table 1. For comparison with previous models, we computed three quantitative measures: Inception Score (IS), Frechet Inception Distance (FID) and the Feature Similarity (FSIM) index. IS measures the sample quality and diversity by finding the entropy of the predicted labels. FID score measures the similarity between real and fake samples by fitting a multivariate Gaussian (MVG) model to the intermediate representation. The FSIM index computes quality estimates based on phase congruency as the primary feature, and incorporates the gradient magnitude as the complementary feature for the real and fake samples, respectively. Table 3 shows the quantitative comparison of the GAN-metric performance for BPGAN [2]}, CAGAN [3]}, CGAN [4]} , WAGAN [5]}, QAGAN [6]} and ours for CelebA dataset.
